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简介
Causality: Models, Reasoning and Inference 豆 9.2分
资源最后更新于 2020-09-05 22:00:52
作者：Judea Pearl
出版社：Cambridge University Press
出版日期：2009-01
ISBN：9780521895606
文件格式： pdf
标签： Causality 统计 人工智能 数学 方法论 统计学 Statistics 因果推断
简介· · · · · ·
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social scienc...
目录
1. Introduction to probabilities, graphs, and causal models;
2. A theory of inferred causation;
3. Causal diagrams and the identification of causal effects;
4. Actions, plans, and direct effects;
5. Causality and structural models in social science and economics;
6. Simpson's paradox, confounding, and collapsibility;
7. The logic of structure-based counterfactuals;
8. Imperfect experiments: bounding effects and counterfactuals;
9. Probability of causation: interpretation and identification;
10. The actual cause.
2. A theory of inferred causation;
3. Causal diagrams and the identification of causal effects;
4. Actions, plans, and direct effects;
5. Causality and structural models in social science and economics;
6. Simpson's paradox, confounding, and collapsibility;
7. The logic of structure-based counterfactuals;
8. Imperfect experiments: bounding effects and counterfactuals;
9. Probability of causation: interpretation and identification;
10. The actual cause.
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